论文标题
使用机器学习技术从SAR虚构的自动目标识别（ATR）
Automatic Target Recognition (ATR) from SAR Imaginary by Using Machine Learning Techniques
论文作者
论文摘要
合成孔径雷达（SAR）图像中的自动目标识别（ATR）由于包含高级别的噪声而成为一个非常具有挑战性的问题。在这项研究中，提出了一种基于机器学习的方法，用于使用SAR图像检测不同的移动和固定目标。一阶统计（FOS）特征是从快速傅立叶变换（FFT），离散余弦变换（DCT）和离散小波变换（DWT）中获得的。还使用了灰度级别的共发生矩阵（GLCM），灰度级运行长度矩阵（GLRLM）和灰度尺寸尺寸区域矩阵（GLSZM）算法。这些功能作为使用高斯内核的训练和测试阶段支持向量机（SVM）模型提供的输入。在绩效评估中实施了4倍的交叉验证。获得的结果表明，GLCM + SVM算法是最佳模型，精度为95.26％。该提出的方法表明，MSTAR数据库中的移动和固定目标可以通过高性能识别。
Automatic Target Recognition (ATR) in Synthetic aperture radar (SAR) images becomes a very challenging problem owing to containing high level noise. In this study, a machine learning-based method is proposed to detect different moving and stationary targets using SAR images. First Order Statistical (FOS) features were obtained from Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on gray level SAR images. Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM) and Gray Level Size Zone Matrix (GLSZM) algorithms are also used. These features are provided as input for the training and testing stage Support Vector Machine (SVM) model with Gaussian kernels. 4-fold cross-validations were implemented in performance evaluation. Obtained results showed that GLCM + SVM algorithm is the best model with 95.26% accuracy. This proposed method shows that moving and stationary targets in MSTAR database could be recognized with high performance.
